#3 Ring of Fire (14-6)

avg: 2169  •  sd: 99.13  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
2 Truck Stop Loss 7-15 1581.02 Aug 2nd 2019 US Open Club Championship
4 PoNY Win 14-10 2507.12 Aug 2nd 2019 US Open Club Championship
1 Sockeye Loss 11-14 1945.43 Aug 3rd 2019 US Open Club Championship
6 Revolver Win 15-11 2419.73 Aug 3rd 2019 US Open Club Championship
2 Truck Stop Loss 12-15 1880.53 Aug 4th 2019 US Open Club Championship
7 Sub Zero Win 15-7 2585.71 Aug 31st TCT Pro Championships 2019
12 Doublewide Win 14-11 2168.85 Aug 31st TCT Pro Championships 2019
11 Pittsburgh Temper Win 13-8 2357.91 Aug 31st TCT Pro Championships 2019
5 Chicago Machine Loss 12-14 1866.15 Sep 1st TCT Pro Championships 2019
8 DiG Win 15-14 2076.74 Sep 1st TCT Pro Championships 2019
38 Lost Boys Win 13-9 1861.56 Sep 21st Southeast Club Mens Regional Championship 2019
128 Swamp Horse** Win 13-4 1505.54 Ignored Sep 21st Southeast Club Mens Regional Championship 2019
52 El Niño** Win 13-3 1956.84 Ignored Sep 21st Southeast Club Mens Regional Championship 2019
37 Tanasi Win 13-7 2032.78 Sep 22nd Southeast Club Mens Regional Championship 2019
14 Chain Lightning Win 13-10 2146.24 Sep 22nd Southeast Club Mens Regional Championship 2019
12 Doublewide Loss 13-14 1730.51 Oct 24th USA Ultimate National Championships 2019
15 Rhino Slam! Win 15-5 2417.59 Oct 24th USA Ultimate National Championships 2019
6 Revolver Win 15-9 2554.05 Oct 24th USA Ultimate National Championships 2019
2 Truck Stop Win 15-12 2481.51 Oct 25th USA Ultimate National Championships 2019
1 Sockeye Loss 13-14 2133.77 Oct 26th USA Ultimate National Championships 2019
**Blowout Eligible

FAQ

The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)